A survey of clustering algorithms for big data: Taxonomy and empirical analysis

Adil Fahad, Najlaa Alshatri, Zahir Tari, Abdullah Alamri, Ibrahim Khalil, Albert Y. Zomaya, Sebti Foufou, Abdelaziz Bouras

Research output: Contribution to journalArticle

Abstract

Clustering algorithms have emerged as an alternative powerful meta-learning tool to accurately analyze the massive volume of data generated by modern applications. In particular, their main goal is to categorize data into clusters such that objects are grouped in the same cluster when they are similar according to specific metrics. There is a vast body of knowledge in the area of clustering and there has been attempts to analyze and categorize them for a larger number of applications. However, one of the major issues in using clustering algorithms for big data that causes confusion amongst practitioners is the lack of consensus in the definition of their properties as well as a lack of formal categorization. With the intention of alleviating these problems, this paper introduces concepts and algorithms related to clustering, a concise survey of existing (clustering) algorithms as well as providing a comparison, both from a theoretical and an empirical perspective. From a theoretical perspective, we developed a categorizing framework based on the main properties pointed out in previous studies. Empirically, we conducted extensive experiments where we compared the most representative algorithm from each of the categories using a large number of real (big) data sets. The effectiveness of the candidate clustering algorithms is measured through a number of internal and external validity metrics, stability, runtime, and scalability tests. In addition, we highlighted the set of clustering algorithms that are the best performing for big data.

Original languageEnglish (US)
Article number6832486
Pages (from-to)267-279
Number of pages13
JournalIEEE Transactions on Emerging Topics in Computing
Volume2
Issue number3
DOIs
StatePublished - Jan 1 2014

Fingerprint

Taxonomies
Clustering algorithms
Scalability
Big data
Experiments

Keywords

  • big data
  • Clustering algorithms
  • unsupervised learning

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications

Cite this

Fahad, A., Alshatri, N., Tari, Z., Alamri, A., Khalil, I., Zomaya, A. Y., ... Bouras, A. (2014). A survey of clustering algorithms for big data: Taxonomy and empirical analysis. IEEE Transactions on Emerging Topics in Computing, 2(3), 267-279. [6832486]. https://doi.org/10.1109/TETC.2014.2330519

A survey of clustering algorithms for big data : Taxonomy and empirical analysis. / Fahad, Adil; Alshatri, Najlaa; Tari, Zahir; Alamri, Abdullah; Khalil, Ibrahim; Zomaya, Albert Y.; Foufou, Sebti; Bouras, Abdelaziz.

In: IEEE Transactions on Emerging Topics in Computing, Vol. 2, No. 3, 6832486, 01.01.2014, p. 267-279.

Research output: Contribution to journalArticle

Fahad, A, Alshatri, N, Tari, Z, Alamri, A, Khalil, I, Zomaya, AY, Foufou, S & Bouras, A 2014, 'A survey of clustering algorithms for big data: Taxonomy and empirical analysis', IEEE Transactions on Emerging Topics in Computing, vol. 2, no. 3, 6832486, pp. 267-279. https://doi.org/10.1109/TETC.2014.2330519
Fahad, Adil ; Alshatri, Najlaa ; Tari, Zahir ; Alamri, Abdullah ; Khalil, Ibrahim ; Zomaya, Albert Y. ; Foufou, Sebti ; Bouras, Abdelaziz. / A survey of clustering algorithms for big data : Taxonomy and empirical analysis. In: IEEE Transactions on Emerging Topics in Computing. 2014 ; Vol. 2, No. 3. pp. 267-279.
@article{0dc47abe8440470c9ad600d645e38095,
title = "A survey of clustering algorithms for big data: Taxonomy and empirical analysis",
abstract = "Clustering algorithms have emerged as an alternative powerful meta-learning tool to accurately analyze the massive volume of data generated by modern applications. In particular, their main goal is to categorize data into clusters such that objects are grouped in the same cluster when they are similar according to specific metrics. There is a vast body of knowledge in the area of clustering and there has been attempts to analyze and categorize them for a larger number of applications. However, one of the major issues in using clustering algorithms for big data that causes confusion amongst practitioners is the lack of consensus in the definition of their properties as well as a lack of formal categorization. With the intention of alleviating these problems, this paper introduces concepts and algorithms related to clustering, a concise survey of existing (clustering) algorithms as well as providing a comparison, both from a theoretical and an empirical perspective. From a theoretical perspective, we developed a categorizing framework based on the main properties pointed out in previous studies. Empirically, we conducted extensive experiments where we compared the most representative algorithm from each of the categories using a large number of real (big) data sets. The effectiveness of the candidate clustering algorithms is measured through a number of internal and external validity metrics, stability, runtime, and scalability tests. In addition, we highlighted the set of clustering algorithms that are the best performing for big data.",
keywords = "big data, Clustering algorithms, unsupervised learning",
author = "Adil Fahad and Najlaa Alshatri and Zahir Tari and Abdullah Alamri and Ibrahim Khalil and Zomaya, {Albert Y.} and Sebti Foufou and Abdelaziz Bouras",
year = "2014",
month = "1",
day = "1",
doi = "10.1109/TETC.2014.2330519",
language = "English (US)",
volume = "2",
pages = "267--279",
journal = "IEEE Transactions on Emerging Topics in Computing",
issn = "2168-6750",
publisher = "IEEE Computer Society",
number = "3",

}

TY - JOUR

T1 - A survey of clustering algorithms for big data

T2 - Taxonomy and empirical analysis

AU - Fahad, Adil

AU - Alshatri, Najlaa

AU - Tari, Zahir

AU - Alamri, Abdullah

AU - Khalil, Ibrahim

AU - Zomaya, Albert Y.

AU - Foufou, Sebti

AU - Bouras, Abdelaziz

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Clustering algorithms have emerged as an alternative powerful meta-learning tool to accurately analyze the massive volume of data generated by modern applications. In particular, their main goal is to categorize data into clusters such that objects are grouped in the same cluster when they are similar according to specific metrics. There is a vast body of knowledge in the area of clustering and there has been attempts to analyze and categorize them for a larger number of applications. However, one of the major issues in using clustering algorithms for big data that causes confusion amongst practitioners is the lack of consensus in the definition of their properties as well as a lack of formal categorization. With the intention of alleviating these problems, this paper introduces concepts and algorithms related to clustering, a concise survey of existing (clustering) algorithms as well as providing a comparison, both from a theoretical and an empirical perspective. From a theoretical perspective, we developed a categorizing framework based on the main properties pointed out in previous studies. Empirically, we conducted extensive experiments where we compared the most representative algorithm from each of the categories using a large number of real (big) data sets. The effectiveness of the candidate clustering algorithms is measured through a number of internal and external validity metrics, stability, runtime, and scalability tests. In addition, we highlighted the set of clustering algorithms that are the best performing for big data.

AB - Clustering algorithms have emerged as an alternative powerful meta-learning tool to accurately analyze the massive volume of data generated by modern applications. In particular, their main goal is to categorize data into clusters such that objects are grouped in the same cluster when they are similar according to specific metrics. There is a vast body of knowledge in the area of clustering and there has been attempts to analyze and categorize them for a larger number of applications. However, one of the major issues in using clustering algorithms for big data that causes confusion amongst practitioners is the lack of consensus in the definition of their properties as well as a lack of formal categorization. With the intention of alleviating these problems, this paper introduces concepts and algorithms related to clustering, a concise survey of existing (clustering) algorithms as well as providing a comparison, both from a theoretical and an empirical perspective. From a theoretical perspective, we developed a categorizing framework based on the main properties pointed out in previous studies. Empirically, we conducted extensive experiments where we compared the most representative algorithm from each of the categories using a large number of real (big) data sets. The effectiveness of the candidate clustering algorithms is measured through a number of internal and external validity metrics, stability, runtime, and scalability tests. In addition, we highlighted the set of clustering algorithms that are the best performing for big data.

KW - big data

KW - Clustering algorithms

KW - unsupervised learning

UR - http://www.scopus.com/inward/record.url?scp=84908602473&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84908602473&partnerID=8YFLogxK

U2 - 10.1109/TETC.2014.2330519

DO - 10.1109/TETC.2014.2330519

M3 - Article

VL - 2

SP - 267

EP - 279

JO - IEEE Transactions on Emerging Topics in Computing

JF - IEEE Transactions on Emerging Topics in Computing

SN - 2168-6750

IS - 3

M1 - 6832486

ER -